1 Introduction

The manifold ecological and societal benefits urban trees provide (e.g., Roy et al., 2012) depend critically on their health and performance. For instance, trees alter local energy budgets (Grimmond et al., 1996 ; Hertel and Schlink, 2019) through shading and transpiration (Endlicher et al., 2016; Gillner et al., 2015), and therefore can reduce ambient temperatures, infrastructure power-consumption and (human) thermal discomfort (Akbari et al., 2001; e.g. Gulyás et al., 2006; Hoyano, 1988; Mayer and Höppe, 1987). However, excess heat common for cities (i.e., Urban Heat Island, UHI, Oke, 1982), combined with other urban conditions, affects tree physiological functioning with outcomes ranging from enhanced growth to early senescence, branch die-back, and even mortality (Au, 2018; Gillner et al., 2014; e.g., Hilbert et al., 2019). Thus, assessing the effect of increased temperatures on trees, as part of urban green infrastructure, is instrumental for understanding as well as adapting to current and expected conditions in this century (Ward and Johnson, 2007), especially considering ever more urbanized societies and the potential for UHI effects to compound with more frequent atmospheric drought (Brune, 2016; Norton et al., 2015; Roloff et al., 2009).

The UHI effect, i.e., the difference between urban and adjacent rural (air) temperatures, has been intensively studied for several decades (cf. Oke, 1982; Stewart, 2011). It is typically related to the structure and density of urban land-use (Kuttler et al., 2015), which can be characterized through local climate zones, and modulated by physiographic and urban characteristics, such as vicinity to water bodies, predominant wind and street direction, etc. (Stewart and Oke, 2012); yet, the physical basis for the excess heat in cities is to a large extent found in the altered surface energy balance as the proportional cover of vegetation decreases compared to rural (or reference) systems (Hertel and Schlink, 2019; Oke, 1992). In temperate climates, this results in strongest UHI magnitudes at night (cf. Fenner et al., 2014). For example, Berlin features the most intense UHI in Germany due to its large extent and development intensity with an average air temperature increase of around 5 K at night-times (2001-2010) with maxima of up to 11 K (Fenner et al., 2014) in urban \(vs.\) rural areas.

Increased air temperatures due to UHIs can affect tree growth through altering several physiological processes across plant organs directly or indirectly (Dusenge et al., 2019). Generally, reaction times at cellular level increase with temperature up to a maximum, after which a drop in enzymatic activity results in a species-dependent optimum curve (Arcus et al., 2016; Parent et al., 2010). In leaves this optimum response is reflected in the net assimilation rate of carbohydrates, as a balance of photosynthesis and respiration, with losses exceeding gains more rapidly with increasing temperatures (Long, 1991). These responses vary between species (Tjoelker et al., 2001) as well as intra-specifically due to local acclimation, i.e., a shift of optimum temperature responses after prolonged exposure (Yamori et al., 2014), and threshold temperatures before tissue damage occurs (for review see Geange et al., 2021). High temperatures in temperate areas are often coincident with low relative air humidity (i.e., large vapor pressure deficit), which in turn can decrease stomatal conductance governing the majority of gas exchange in leaves (Grossiord et al., 2020), and thus the capacity for photosynthesis. Under prolonged stomatal closure (or decreased conductance) with high temperatures, trees may thus face decreased growth (in subsequent years) or even starvation as their carbohydrate reserves are depleted yet not replenished at sufficient rates (McDowell et al., 2008). Furthermore, air (and soil temperatures) affect the initiation, speed and cessation of cambial activity, and thus radial growth throughout a growing season (e.g., see Begum et al., 2013; Rathgeber et al., 2016). Radial growth is increasingly considered to be limited by wood formation dynamics and their relation with environmental drivers, rather than solely by photosynthetic activity (Körner, 2015). In particular, the availability of soil water is critical for cell expansion (e.g., Peters et al., 2021) and most likely limits radial growth before photosynthesis (Fatichi et al., 2014); however, this water availability is again linked to local climate as higher temperatures drive evaporation and thus may contribute to the depletion of soil water storage, impeding growth.

Urban trees show a tendency for enhanced growth rates and/or productivity compared to rural conspecifics, which is typically attributed to increased temperatures (Jia et al., 2018; Pretzsch et al., 2017), yet feature a broad range of effect size ranges and signs (i.e., reduced growth) specific to species and location. Zhao et al. (2016) showed that productivity rates, as a proxy for growth, increased within urban clusters as urbanization intensifies using remotely sensed vegetation indices. Further, Moser-Reischl et al. (2019) identified positive associations between air temperature and radial growth for two species (total of 20 individuals) commonly selected by urban planners (T. cordata, Rubinia pseudoacacia) in Munich.

(Briber et al., 2015; O’Brien et al., 2012) … generally increased growth Contrastingly, Gillner et al. (2014) highlight decreased growth for Acer species (A. platanoides and pseudoplatanus), Platanus x hispanica and Quercus rubra with higher summer temperatures of the preceding year, especially when compounded with drought, in another German metropolis (Dresden). Quigley (2004) identified absolute growth potential decreased for species between rural and urban conspecifics, yet was limited to comparatively small sample sizes per group (\(n_{total}~=~230\) divided in 15 species, 3 groups and 2 locations) - also lacked multi-modal responses Pretzsch et al. (2017) inferred enhanced growth in recent decades and across urban locations spanning several latitudes, including Berlin - however, only 145 individuals of one species (T. cordata) were assessed there. As mentioned previously, climate-growth relationships can vary substantially between species, and in fact, Quigley (2004) and Pretzsch et al. (2017) report contrasting results regarding average tree diameter, i.e. smaller or larger for urban \(vs.\) rural trees of same age, consider other studies mentioned above Similarly, for Berlin, Dahlhausen et al. (2018), identified enhanced growth in highly urbanized environments (using basal area increments of 252 trees) for Tilia cordata Mill, the most abundant tree of the city, which they attributed to the UHI effect, while intermediate development intensity was adverse for tree growth.

These differences in growth trends may result from contrasting species-specific responses to increased temperatures, but are indeed affected by other (time-varying) factors and stochastic processes, such as water availability, pollution and road-salt loading, structural impedance by infrastructure, or management, etc. (Pauleit et al., 2002; Quigley, 2004; Randrup et al., 2001; Rhoades and Stipes, 1999). expand on location-specific factors that can affect growth This can hinder the extrapolation from individual sampling sites toward predicting effect across entire urban areas and tree stocks, especially where studies rely on labor-intensive methods which are limited logistically by sampling effort, reducing sample sizes, and species as well as spatial coverage. This is exacerbated by a lack of co-located environmental variables (i.e. measured in situ) at pertinent spatial scales, for instance, as noted by Wohlfahrt et al. (2019) for air temperature and tree leaf phenology, which may lead to incorrect inferences and interpretations for the role of climate change on growth/productivity when applying space-for-time substitutions.

Space-for-time substitutions are a common approach (cf. studies above) to generate inferences in observational (rather than treatment-control) studies, where manipulations are costly or logistically infeasible due to time and/or financial constraints.
However, they require accounting for confounding factors specific to trees’ environments, such as street characteristics, development intensity, available soil volume, etc. While several of the aforementioned studies applied the space-for-time approach to quantify temperature and excess heat on growth, they typically compare trees grouped using qualitative or summary descriptors of sampling sites, disregarding the spatio-temporal variability in location-specific factors noted above. This can hinder the extrapolation from individual sampling sites toward predicting effects across entire urban areas and tree stocks, especially when studies rely on labor-intensive methods which are limited logistically by sampling effort, reducing sample sizes, and species as well as spatial coverage. This can be exacerbated by a lack of co-located environmental variables (i.e. measured in situ) at pertinent spatial scales, for instance, as noted by Wohlfahrt et al. (2019) for air temperature and tree leaf phenology, which may lead to incorrect inferences and interpretations for the role of climate change on growth/productivity when applying space-for-time substitutions. It is thus likely that the varying and even contrasting growth responses observed for urban trees across and within studies is at least modulated by several confounding factors, making the attribution to a single driver, such as excess heat, more difficult and possibly less accurate.

These limitations could be overcome by relying on large data sets with extensive ancillary data coverage. Berlin, as one of the greenest cities in Europe, provides an openly accessible tree inventory, with spatio-temporal environmental data sets pertinent to tree growth, through its Environmental Agency. It features a total of 650000 individuals covering 94 genera and at least 600 species and/or cultivars, listing information on location, trunk diameter (at breast height; \(DBH\); see Tab.\(~\)??), and stem height, amongst other variables, for the majority of street and park trees. For this study, our object was to assess the impact of excess urban heat, i.e. the UHI effect, on tree growth using this openly available inventory data set, complemented by additional open data sources as well as incremental growth data from tree cores. The assessment relied on flexible statistical models that could capture species and location-specific responses to heat and other urban factors. Specifically, we aimed to (1) assess heat exposure of most abundant species; (2) determine the impact of (excess) heat on stem growth with space-for-time substitution to increase coverage; (3) h ighlight the role of location-specific environmental factors in mediating temperature responses. While this work constitutes a case study for Berlin, we believe the results are a valuable contribution toward Berlin’s current and future management of its tree stock. This is because the high temporal and spatial variability of controls on growth, and thus variability in responses, require that assessments are developed for a specific region, because well-understood tree characteristics (e.g., see Brune, 2016; Roloff et al., 2009), could be strongly modulated in predicable ways at a given location or time due to management, planting practices, or other environmental controls; for example, if drought hardiness is related to extensive root networks, small soil volumes available to street trees could render a species vulnerable to water stress. As such, the approach can also serve as a framework for base-line assessments for other cities with available or growing inventory and ancillary data [cite something on tree diversity in tree stocks, management, etc.], and also inform species-climate matrices regarding temperature sensitivity…..

–>

–> –> –> –> –> –>

–>

In a space-for-time substitution, growth of individual species can be assessed across the entire city of Berlin, and related to effects of the UHI, while accounting for other location-specific factors, such as street characteristics, development intensity, available soil volume, etc. Comparable applications are found, for example, in Quigley (2004) and Pretzsch et al. (2017). The former inferred absolute growth potential for species across successional groups (early, mid, late stage), and between rural and urban conspecifics, yet lacked spatially-explicit effect size estimates across the urban-rural space and was limited to comparatively small sample sizes per group (\(n_{total}~=~230\) divided in 15 species, 3 groups and 2 locations). Pretzsch et al. (2017) applied linear hierarchical models to infer growth modulation on annual basis for different time periods and urban \(vs.\) rural locations while accounting for stand-level variability; however, for Berlin only 145 individuals of one species (T. cordata) were assessed. As mentioned previously, climate-growth relationships can vary substantially between species, and in fact, Quigley (2004) and Pretzsch et al. (2017) report contrasting results regarding average tree diameter, i.e. smaller or larger for urban \(vs.\) rural trees of same age. Consequently, this variability of effect sizes and directions calls for a more comprehensive assessment across species and with greater spatial coverage throughout Berlin.

We therefore propose applying a statistical model that is fully spatially explicit, while also allowing to account for the nested nature of the data set (e.g. streets and districts) as well as other pertinent factors using hierarchical, generalized additive models (see Section\(~\)2). As a result, the absolute growth potential of a species can be inferred given, for example, a specific location, age or UHI magnitude. Further, the impact of UHI loading can be predicted for a single species across all of Berlin as a continuous surface. The inclusion of independent, tree-level growth data, however, is paramount as it allows:

  1. validating the publicly-available Senate dataset (\(DBH\)) while providing a more reliable estimate of tree age,
  2. applying the model with annual basal area as a response (enabling incorporating effects of varying climate/UHI intensities over time), and
  3. comparing \(DBH\) (Senate data) and basal area-derived models for a subset of locations and species, increasing confidence in Berlin-wide predictions.

2 Methods

2.1 Study area

Berlin is one of the largest metropolitan areas in Central Europe (892\(~km^2\)) with a population of approximately 3.6 million, and a maximum extent of 38\(~km\) in North-South and 45\(~km\) in East-West directions. It is located in North-Eastern Germany, and lies in the temperate zone with warm-humid climate (Dfb) according to the updated Köppen-Geiger classification (Beck et al., 2018), with mean annual temperature of approximately 10\(^\circ C\) and precipitation of 575\(~mm\) (Tempelhof weather station, DWD). Berlin features low relief (approximately 30\(~m\) to 60\(~m\) with 120\(~m\) at solitary peaks), and is centered around a glacial outwash valley (sands, gravel), bordered by two plateaus consisting of glacial till and clay in the North-East and South, as well as sands in the South-West. The city provides extensive public green space covering around 30\(~\%\) of its area (SUVK, Berlin, 2019), with an extensive urban forest of nearly 700000 publicly-managed trees along streets, in parks and in riparian areas.

2.2 Data sources

An overview of data used for models, including sources, types, and application, is provided in Table\(~\)2.2.

2.2.1 Street trees

Characteristics of street trees and their respective environments were collated from the city’s tree inventory, as well as various elements of the Berlin Environment Atlas, which are openly available via https://daten.berlin.de and curated by the city’s government. The data used from the inventory includes species, location, age, and diameter at breast height (dbh). quality control - filtered dataset to remove drastic outliers –> fit poly model, used Median Absolute deviance with multiplier of X to exclude points. Focused on 9 most abundant species to and Cultivars and hybrids were combined to increase sample sizes. check which ones

Table\(~\)2.1 shows the binned distribution of genera across age classes.

Table 2.1: Binned age-distribution for trees in Berlin data set, and entries missing age information.
Genera (0,30] (30,60] (60,90] (90,120] (120,150] 150+ Total (n) Missing (n)
Tilia 40128 60854 34599 4390 120 11 140232 130
Acer 23306 33771 10220 1798 62 17 69330 156
Quercus 8686 16107 5721 2595 562 157 33873 45
Platanus 4467 11836 4784 1449 805 68 23425 16
Aesculus 4464 7064 5566 1211 91 25 18427 6
Betula 2469 7155 897 36 2 1 10572 12
Fraxinus 4324 3332 742 131 6 0 8543 8
Robinia 2494 4523 857 83 3 1 7975 14
Carpinus 3905 2349 176 4 0 0 6466 32
Prunus 3792 2121 111 12 0 0 6067 31
Populus 639 3559 991 279 17 14 5515 16
Pinus 422 1349 463 27 0 1 2269 7
Other 22337 12620 1799 448 61 17 37554 272
Marg. Totals 121433 166640 66926 12463 1729 312 370248 745

2.2.2 Temperature/UHI data sets

Temperature and UHI data were summarized temporally either by the provider or manually to provide a characteristic representation of heat loading during summer at different times (morning, afternoon/day, night), from which tree averages (radius of 150\(~m\)) were calculated. Two data sets of urban air and one surface temperature were tested as explanatory variables in GAMM models. The air temperatures from the Berlin environmental atlas (EnvAt) were model outputs that are representations of typical summer conditions at 0400, 1400 and 2200 hours; these data are provided at city block basis (spatial polygons), from which weighted averages were extracted. UrbClim air temperatures are hourly model outputs (100\(~m\) resolution, ref) based on ERA5 re-analyses data (ECMWF) for which data of the hottest month in the record (June, 2011) were averaged to hours equivalent to EnvAt data by using a window of \(\pm~1~\)hour (i.e., 0300 to 0500, etc.). Subsequently, a land-use and landcover mask (2018, Copernicus CORINE ref) was used to define urban and rural/forested areas, by which the urban heat loading was calculated as \(UHI_{x,y} = T_{Air_{2m}~x,y} - \overline{T_{Air_{2m}~Rural}}\), where \(x\) and \(y\) define an urban grid cell. The surface UHI data set by Chakraborty and Lee (2019) derives its measure in a similar fashion and the reader is referred to the detailed description therein; note this data set provides day and night-time averaged UHI estimates at 500\(~m\) resolution, which were extracted for the hottest summer in this record (2007).

2.2.3 Ancillary data sets

Following the general approach described above, four ancillary dependent variables next to a temperature measure were employed in models; these were chosen due to their availability at high spatial resolution and coverage, and because their influence on growth was previously identified in literature or their likely impact could be deduced using ecophysiological principles. We included planting bed area, the sum of exchangeable basic cation as a proxy for soil nutrient availability, adjacent building height, and the proportional coverage of local climate zone 6 (LCZ6; open mid-rise, see Demuzere et al. (2019) and Stewart and Oke (2012) for details). The latter was chosen as an increase reflects a transition away from densely urbanized areas. Note that only street trees in urban, not rural areas or within greenspaces, were considered here, but individual trees may grow along streets adjacent to greenspaces and parks of varying sizes.

were considered to have The environment atlas also provided soil nutrient exchange capacity as a measure of growth-favoring soil conditions,

surface area of tree planting beds (“Baumscheibe”), as well as general characteristics of the urban environment like building height. It also includes spatially-explicit model outputs for local climate simulations representative of typical conditions, such as air temperature (2 m) on a 2015 summer’s day at different hours (0400, 1400, 2200). These model outputs were preferred over in situ measurements as more representative based on averaged conditions, physiologically more relevant than surface urban heat island products. These outputs are available at city block or planning unit level, and were used as a proxy for the general temperature distribution influenced by urbanization across the investigated growth period across the previous 80 years (see section X for details). This was deemed reasonable as Berlin’s built-up area has not changed markedly over the past 50 years, i.e., about 52 to 61\(\%\) (Mohamed, 2017). Further insight into the role of the urban environment was assessed by including local climate zone classes derived by WUDAPT. These describe the proportion of urban area with a specific cover, such as dense high rises, sealed surfaces, etc. For this study, the LCZ 6, representing dense mid-rise built up cover was used as a proxy for the degree of urbanization, and is the most frequent LCZ for Berlin (Fenner et al., 2017) (found ref in . Use percent sealed surface instead?

Table 2.2: Data description used for maps/visualizations and analyses. Resolution and radius are provided in \(m\), the latter is the buffer in which data was averaged around each trees. A zero-radius refers to a point extraction from categorical and location specific data.**
Name Accessed Type Unit Resolution Radius Source Reference
Street Trees Oct ’20 Point https://daten.berlin.de/
UHI Berlin Dec ’19 Raster \(^\circ C\) 500 150 https://yceo.yale.edu/research/global-surface-uhi-explorer Chakraborty et al. (2019)
UHI Berlin Dec ’19 Raster \(^\circ C\) 500 150 https://yceo.yale.edu/research/global-surface-uhi-explorer
Berlin Climate Model, Air temperature 2015 (Umweltatlas) Feb ’21 Polygon \(^\circ C\) 20 https://daten.berlin.de/
UrbClim ERA5 Model Output (ECMWF, UCSC) Mar ’21 Raster \(^\circ C\) 100 150 https://cds.climate.copernicus.eu/ Deridder et al. (2015)
Berlin Land-use Apr ’21 Polygon https://daten.berlin.de/
Copernicus CORINE CLC Mar ’21 Raster 100 https://land.copernicus.eu/
WUDAPT LCZ Oct ’20 Raster 100 150/300 https://www.wudapt.org/continental-lcz-maps/ Demuzere et al. (2019)
Berlin Veg/Building Height Oct ’20 Polygon \(m\) 150/300 https://daten.berlin.de/
Berlin Soil Nutrients, Bodenkundliche Kennwerte 2015 (Umweltatlas) Nov ’20 Polygon \(mol~m^{-2}\) 0 https://daten.berlin.de/
Planting Bed Area Oct ’20 Polygon \(m^2\) 0 https://daten.berlin.de/
Berlin Soils Oct ’20 Polygon 0 https://daten.berlin.de/
Berlin Districts Oct ’20 Polygon https://daten.berlin.de/
Berlin Transport Network Feb ’21 Polygon OpenStreetMap Overpass API
Berlin Water (Ways) Feb ’21 Polygon OpenStreetMap Overpass API

2.3 Dendrochronological sampling and analyses

2.4 Statistical Analyses

2.4.1 General approach: space-for-time analyses

2.4.1.1 Model development and selection

2.5 General analyses

The proposed statistical method is from the class of hierarchical, generalized additive models (GAM, or GAMM for mixed models/hierarchical models). In these models combinations of continuous and categorical predictor variables can be summed to estimate a response. In particular, continuous variables that are linearly, as well as non-linearly related to the response can be represented by applying a transfer function, typically termed “smoothing function” (Wood, 2017); these are constructed using a number of base functions of varying complexity and form, which provides a high degree of flexibility, ideal for fitting ecosystem dynamics which are rarely linear (Pedersen et al., 2019), or correctly represented with deterministic functional forms (e.g. quadratic equations). In general, a GAM can be written as:

\[\begin{equation} E (Y)~=~g^{-1}\left( \beta_0 + \sum_{i = 1}^{n} f_i (x_i) \right), \tag{2.1} \end{equation}\]

and

\[\begin{equation} y~=~E (Y) + \epsilon, \tag{2.2} \end{equation}\]

where \(Y\) is taken from an appropriate distribution and corresponding link function \(g\), \(\beta_0\) is the intercept and \(f_i\) represents a smooth function of a predictor (Pedersen et al., 2019), and \(\epsilon \sim \mathcal{N}(0, \sigma ^2)\). Note, that \(f_i\) consists of a smooth (e.g. spline) constructed via basis functions of different form and complexity, multiplied by a coefficient:

\[\begin{equation} f_i(x_i)~=~\sum_{k = 1}^{K} \beta_{i, k} b_{i,k}(x_i). \tag{2.3} \end{equation}\]

Nested data structures (e.g. due to similar road [type]) can be accounted for by introducing random effects (Wood, 2017), while spatial dependence between observations can be included by constructing smoothing functions with e.g. northings and eastings, as for example done in (Augustin et al., 2009). Ultimately, the implementation of a such a GAMM will allow for establishing continuous prediction surfaces of growth potential (approximated via \(DBH\)) for individual species across urban areas (including parks) of Berlin.

Currently, \(DBH\) has been modeled using a hierarchical linear model (linear mixed effects model) with lme4 (Bates et al., 2015) in R Core Team (2020) (see Section\(~\)3). The general form of this model is:

\[\begin{equation} Y_{i,j} = (\beta_0 + b_{0,i,j}) + (\beta_1 + b_{1,i,j}) \cdot x_i + \epsilon_{i,j}, \tag{2.4} \end{equation}\] where \(\beta_0\) is the intercept with its random component \(b_0\), and \(\beta_1\) the slope with its random component \(b_1\). The random errors are assumed i.i.d. and distributed as \(b \sim \mathcal{N}(0, \tau ^2)\). The model for which results are presented in Figure\(~\)3.5 estimates \(DBH\) from tree age and the local UHI intensity as continuous covariates with random slopes and intercepts for each species; note, that for computational efficiency each genera was modeled separately. Further, models were established for the three most abundant species per genera with at least 1000 individuals.

3 Preliminary results

Tree locations are clustered and structured based on their category, i.e. riparian, street and park trees (Fig.\(~\)3.1). Planting in space and time shows species-specific patterns (by districts), often related to major events, such as the start and end of armed and/or political conflict.

Individual tree locations for three categories available in Berlin Senate urban tree data set. Note, that for each category 7000 observations were subsampled from the available pool to facilitate visualization.

Figure 3.1: Individual tree locations for three categories available in Berlin Senate urban tree data set. Note, that for each category 7000 observations were subsampled from the available pool to facilitate visualization.

Gridded counts for the 11 most frequent genera, as well as *Pinus* and remaining genera. Note, that counts are standardized to unity for individual genera.

Figure 3.2: Gridded counts for the 11 most frequent genera, as well as Pinus and remaining genera. Note, that counts are standardized to unity for individual genera.

The distribution of the UHI effect is highly irregular and clustered in space (Fig.\(~\)3.3), and also shows variability through time (data not shown, refer to the urban heat island explorer).

Estimate of UHI intensity based on the algorithm in (Chakraborty and Lee, 2019), comparing urban with rural pixels within the greater metropolitan cluster. Presented values are averaged over the summer of 2007.

Figure 3.3: Estimate of UHI intensity based on the algorithm in (Chakraborty and Lee, 2019), comparing urban with rural pixels within the greater metropolitan cluster. Presented values are averaged over the summer of 2007.

The exposure to increased heat-loading of individual genera (and consequently species) is highly uneven throughout the city (Fig.\(~\)3.4). Street and park trees of most genera are clustered in urban areas with intermediate to high UHI loading, while riparian trees, and some street and park trees of other genera tend to be spread more evenly across Berlin’s UHI range.

Empirical density distribution of all individuals within the presented genera along the UHI continuum. UHI intensities were extracted for each tree location, and the distribution hence represents the first detailed overview of the exposure of Berlin's trees to urban heat loading. The black line is the density across all three categories. Insets show corresponding tree totals.

Figure 3.4: Empirical density distribution of all individuals within the presented genera along the UHI continuum. UHI intensities were extracted for each tree location, and the distribution hence represents the first detailed overview of the exposure of Berlin’s trees to urban heat loading. The black line is the density across all three categories. Insets show corresponding tree totals.

Note, that results below are preliminary and should be considered as a template for future outputs, rather than used for inference. The effect of UHI loading on absolute growth potential varies between genera and species (Fig.\(~\)3.5). Most notably, Quercus, the 3rd-most frequent genera, shows decreased absolute growth with increasing UHI loading, while the most frequent genera, Tilia, features contrasting relationships between species. The estimated effect sizes presented here are linear. However, temperature may exert a non-linear control on absolute growth and, hence, applying a method able to capture such dynamics may result in somewhat different effect sizes / behavior. Additionally, if temperatures increase in the future under climate warming, any non-linear effects may become more enhanced, stressing the need for a more flexible model fit and structure (i.e. using GAMM over linear models-).

Impact of UHI loading on tree diameter (\(DBH\)), accounting for age and inter-specific differences from the linear mixed model (via random slopes and intercepts). Line-ranges are standard errors of predicted effect sizes (i.e. slopes). Differences between street and park trees are considerable for some species, and may be due to local clustering and/or spatial under-representation across the UHI continuum. Further investigations need to address the degree of spatial autocorrelation and account for it where required in linear mixed models, and with smoothing interactions in a GAMM implementation.

Figure 3.5: Impact of UHI loading on tree diameter (\(DBH\)), accounting for age and inter-specific differences from the linear mixed model (via random slopes and intercepts). Line-ranges are standard errors of predicted effect sizes (i.e. slopes). Differences between street and park trees are considerable for some species, and may be due to local clustering and/or spatial under-representation across the UHI continuum. Further investigations need to address the degree of spatial autocorrelation and account for it where required in linear mixed models, and with smoothing interactions in a GAMM implementation.

4 Discussion

  1. Temperature, environmental and urban controls on tree growth

    • Intra specific differences
      • management
      • root vs. canopy (huber value?)
      • comparison young vs. old -> increased CO2, perhaps limitation of space for time, as lower conductance under high CO2 may result in more water savings (check WUE in old vs. young trees? future direction).(Dusenge et al., 2019) (McCarthy et al., 2011)
    • Sensitive trees in hot spots
    • Temporal dynamics
    • Validity and limitations

    (Bussotti et al., 2014)

  2. Implications:

5 Outlook

We seek to build upon and improve the current analysis by:

6 References

Akbari, H., Pomerantz, M., Taha, H., 2001. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy, Urban Environment 70, 295–310. https://doi.org/10.1016/S0038-092X(00)00089-X
Arcus, V.L., Prentice, E.J., Hobbs, J.K., Mulholland, A.J., Van der Kamp, M.W., Pudney, C.R., Parker, E.J., Schipper, L.A., 2016. On the Temperature Dependence of Enzyme-Catalyzed Rates. Biochemistry 55, 1681–1688. https://doi.org/10.1021/acs.biochem.5b01094
Au, K.N., 2018. An integrated approach to tree stress monitoring. Arborist News, International Society of Arboriculture 27, 28–31.
Augustin, N.H., Musio, M., von Wilpert, K., Kublin, E., Wood, S.N., Schumacher, M., 2009. Modeling Spatiotemporal Forest Health Monitoring Data. Journal of the American Statistical Association 104, 899–911. https://doi.org/10.1198/jasa.2009.ap07058
Bates, D., Mächler, M., Bolker, B., Walker, S., 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48. https://doi.org/10.18637/jss.v067.i01
Beck, H.E., Zimmermann, N.E., McVicar, T.R., Vergopolan, N., Berg, A., Wood, E.F., 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5, 180214. https://doi.org/10.1038/sdata.2018.214
Begum, S., Nakaba, S., Yamagishi, Y., Oribe, Y., Funada, R., 2013. Regulation of cambial activity in relation to environmental conditions: Understanding the role of temperature in wood formation of trees. Physiologia Plantarum 147, 46–54. https://doi.org/10.1111/j.1399-3054.2012.01663.x
Briber, B.M., Hutyra, L.R., Reinmann, A.B., Raciti, S.M., Dearborn, V.K., Holden, C.E., Dunn, A.L., 2015. Tree Productivity Enhanced with Conversion from Forest to Urban Land Covers. PLOS ONE 10, e0136237. https://doi.org/10.1371/journal.pone.0136237
Brune, M., 2016. Urban trees under climate change. Potential impacts of dry spells and heat waves in three German regions in the 2050s (No. Report 24). Climate Service Center Germany, Hamburg.
Bussotti, F., Pollastrini, M., Killi, D., Ferrini, F., Fini, A., 2014. Ecophysiology of urban trees in a perspective of climate change. Agrochimica 58, 247–268.
Chakraborty, T., Lee, X., 2019. A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. International Journal of Applied Earth Observation and Geoinformation 74, 269–280. https://doi.org/10.1016/j.jag.2018.09.015
Dahlhausen, J., Rötzer, T., Biber, P., Uhl, E., Pretzsch, H., 2018. Urban climate modifies tree growth in Berlin. Int J Biometeorol 62, 795–808. https://doi.org/10.1007/s00484-017-1481-3
Demuzere, M., Bechtel, B., Middel, A., Mills, G., 2019. Mapping Europe into local climate zones. PLOS ONE 14, e0214474. https://doi.org/10.1371/journal.pone.0214474
Dusenge, M.E., Duarte, A.G., Way, D.A., 2019. Plant carbon metabolism and climate change: Elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. New Phytologist 221, 32–49. https://doi.org/10.1111/nph.15283
Endlicher, W., Scherer, D., Büter, B., Kuttler, W., Mathey, J., Schneider, C., 2016. Stadtnatur fördert gutes Stadtklima, in: Ökosystemleistungen in Der Stadt – Gesundheit Schützen Und Lebensqualität Erhöhen, 3.1. TEEB DE. TU Berlin, UFZ Leipzig, Berlin, Leipzig, pp. 51–63.
Fatichi, S., Leuzinger, S., Körner, C., 2014. Moving beyond photosynthesis: From carbon source to sink-driven vegetation modeling. New Phytologist 201, 1086–1095. https://doi.org/10.1111/nph.12614
Fenner, D., Meier, F., Bechtel, B., Otto, M., Scherer, D., 2017. Intra and inter ‘local climate zone’ variability of air temperature as observed by crowdsourced citizen weather stations in Berlin, Germany. metz 26, 525–547. https://doi.org/10.1127/metz/2017/0861
Fenner, D., Meier, F., Scherer, D., Polze, A., 2014. Spatial and temporal air temperature variability in Berlin, Germany, during the years 2001–2010. Urban Climate, ICUC8: The 8th International Conference on Urban Climate and the 10th Symposium on the Urban Environment 10, 308–331. https://doi.org/10.1016/j.uclim.2014.02.004
Geange, S.R., Arnold, P.A., Catling, A.A., Coast, O., Cook, A.M., Gowland, K.M., Leigh, A., Notarnicola, R.F., Posch, B.C., Venn, S.E., Zhu, L., Nicotra, A.B., 2021. The thermal tolerance of photosynthetic tissues: A global systematic review and agenda for future research. New Phytologist 229, 2497–2513. https://doi.org/10.1111/nph.17052
Gillner, S., Bräuning, A., Roloff, A., 2014. Dendrochronological analysis of urban trees: Climatic response and impact of drought on frequently used tree species. Trees 28, 1079–1093. https://doi.org/10.1007/s00468-014-1019-9
Gillner, S., Vogt, J., Tharang, A., Dettmann, S., Roloff, A., 2015. Role of street trees in mitigating effects of heat and drought at highly sealed urban sites. Landscape and Urban Planning 143, 33–42. https://doi.org/10.1016/j.landurbplan.2015.06.005
Grimmond, C., Souch, C., Hubble, M., 1996. Influence of tree cover on summertime surface energy balance fluxes, San Gabriel Valley, Los Angeles. Clim. Res. 6, 45–57. https://doi.org/10.3354/cr006045
Grossiord, C., Buckley, T.N., Cernusak, L.A., Novick, K.A., Poulter, B., Siegwolf, R.T.W., Sperry, J.S., McDowell, N.G., 2020. Plant responses to rising vapor pressure deficit. New Phytologist 226, 1550–1566. https://doi.org/10.1111/nph.16485
Gulyás, Á., Unger, J., Matzarakis, A., 2006. Assessment of the microclimatic and human comfort conditions in a complex urban environment: Modelling and measurements. Building and Environment 41, 1713–1722. https://doi.org/10.1016/j.buildenv.2005.07.001
Hertel, D., Schlink, U., 2019. Decomposition of urban temperatures for targeted climate change adaptation. Environmental Modelling & Software 113, 20–28. https://doi.org/10.1016/j.envsoft.2018.11.015
Hilbert, D.R., Roman, L.A., Koeser, A.K., Vogt, J., Doorn, N.S.0pt0pt0pt0pt. van, 2019. Urban tree mortality: What the literature shows us. Arborist News.0pt0pt0pt0pt Oct: 22-26. Oct, 22–26.
Hoyano, A., 1988. Climatological uses of plants for solar control and the effects on the thermal environment of a building. Energy and Buildings 11, 181–199. https://doi.org/10.1016/0378-7788(88)90035-7
Jia, W., Zhao, S., Liu, S., 2018. Vegetation growth enhancement in urban environments of the Conterminous United States. Global Change Biology 24, 4084–4094. https://doi.org/10.1111/gcb.14317
Körner, C., 2015. Paradigm shift in plant growth control. Current Opinion in Plant Biology 25, 107–114. https://doi.org/10.1016/j.pbi.2015.05.003
Kuttler, W., Miethke, A., Dütemeyer, D., Barlag, A.-B. (Eds.), 2015. Das klima von essen = the climate of essen. Westarp Wiss., Hohenwarsleben.
Long, S.P., 1991. Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: Has its importance been underestimated? Plant, Cell & Environment 14, 729–739. https://doi.org/10.1111/j.1365-3040.1991.tb01439.x
Mayer, H., Höppe, P., 1987. Thermal comfort of man in different urban environments. Theor Appl Climatol 38, 43–49. https://doi.org/10.1007/BF00866252
McCarthy, H.R., Pataki, D.E., Jenerette, G.D., 2011. Plant water-use efficiency as a metric of urban ecosystem services. Ecological Applications 21, 3115–3127. https://doi.org/10.1890/11-0048.1
McDowell, N., Pockman, W.T., Allen, C.D., Breshears, D.D., Cobb, N., Kolb, T., Plaut, J., Sperry, J., West, A., Williams, D.G., Yepez, E.A., 2008. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New Phytologist 178, 719–739. https://doi.org/10.1111/j.1469-8137.2008.02436.x
Mohamed, M.A., 2017. Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS. NR 08, 353–369. https://doi.org/10.4236/nr.2017.85022
Moser-Reischl, A., Rahman, M.A., Pauleit, S., Pretzsch, H., Rötzer, T., 2019. Growth patterns and effects of urban micro-climate on two physiologically contrasting urban tree species. Landscape and Urban Planning 183, 88–99. https://doi.org/10.1016/j.landurbplan.2018.11.004
Norton, B.A., Coutts, A.M., Livesley, S.J., Harris, R.J., Hunter, A.M., Williams, N.S.G., 2015. Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and Urban Planning 134, 127–138. https://doi.org/10.1016/j.landurbplan.2014.10.018
O’Brien, A.M., Ettinger, A.K., HilleRisLambers, J., 2012. Conifer growth and reproduction in urban forest fragments: Predictors of future responses to global change? Urban Ecosyst 15, 879–891. https://doi.org/10.1007/s11252-012-0250-7
Oke, T.R., 1992. Boundary Layer Climates. Routledge, London; New York.
Oke, T.R., 1982. The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society 108, 1–24. https://doi.org/10.1002/qj.49710845502
Parent, B., Turc, O., Gibon, Y., Stitt, M., Tardieu, F., 2010. Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. Journal of Experimental Botany 61, 2057–2069. https://doi.org/10.1093/jxb/erq003
Pauleit, S., Jones, N., Garcia-Martin, G., Garcia-Valdecantos, J.L., Rivière, L.M., Vidal-Beaudet, L., Bodson, M., Randrup, T.B., 2002. Tree establishment practice in towns and cities – Results from a European survey. Urban Forestry & Urban Greening 1, 83–96. https://doi.org/10.1078/1618-8667-00009
Pedersen, E.J., Miller, D.L., Simpson, G.L., Ross, N., 2019. Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ 7, e6876. https://doi.org/10.7717/peerj.6876
Peters, R.L., Steppe, K., Cuny, H.E., De Pauw, D.J.W., Frank, D.C., Schaub, M., Rathgeber, C.B.K., Cabon, A., Fonti, P., 2021. Turgor – a limiting factor for radial growth in mature conifers along an elevational gradient. New Phytologist 229, 213–229. https://doi.org/10.1111/nph.16872
Pretzsch, H., Biber, P., Uhl, E., Dahlhausen, J., Schütze, G., Perkins, D., Rötzer, T., Caldentey, J., Koike, T., Con, T. van, Chavanne, A., Toit, B. du, Foster, K., Lefer, B., 2017. Climate change accelerates growth of urban trees in metropolises worldwide. Scientific Reports 7, 1–10. https://doi.org/10.1038/s41598-017-14831-w
Quigley, M.F., 2004. Street trees and rural conspecifics: Will long-lived trees reach full size in urban conditions? Urban Ecosystems 7, 29–39. https://doi.org/10.1023/B:UECO.0000020170.58404.e9
R Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Randrup, T.B., McPherson, E.G., Costello, L.R., 2001. A review of tree root conflicts with sidewalks, curbs, and roads. Urban Ecosystems 5, 209–225. https://doi.org/10.1023/A:1024046004731
Rathgeber, C.B.K., Cuny, H.E., Fonti, P., 2016. Biological Basis of Tree-Ring Formation: A Crash Course. Front. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.00734
Rhoades, R.W., Stipes, R.J., 1999. Growth of trees on the Virgina Tech Campus in response to various factors 7.
Roloff, A., Korn, S., Gillner, S., 2009. The Climate-Species-Matrix to select tree species for urban habitats considering climate change. Urban Forestry & Urban Greening 8, 295–308. https://doi.org/10.1016/j.ufug.2009.08.002
Roy, S., Byrne, J., Pickering, C., 2012. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban Forestry & Urban Greening 11, 351–363. https://doi.org/10.1016/j.ufug.2012.06.006
Stewart, I.D., 2011. A systematic review and scientific critique of methodology in modern urban heat island literature. International Journal of Climatology 31, 200–217. https://doi.org/10.1002/joc.2141
Stewart, I.D., Oke, T.R., 2012. Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society 93, 1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1
SUVK, Berlin, 2019. Anteil öffentlicher Grünflächen in Berlin, Grünflächeninformationssystem (GRIS). Senatsverwaltung für Umwelt, Verkehr und Klimaschutz Berlin, Referat Freiraumplanung und Stadtgrün, Berlin.
Tjoelker, M.G., Oleksyn, J., Reich, P.B., 2001. Modelling respiration of vegetation: Evidence for a general temperature-dependent Q10. Global Change Biology 7, 223–230. https://doi.org/10.1046/j.1365-2486.2001.00397.x
Ward, K.T., Johnson, G.R., 2007. Geospatial methods provide timely and comprehensive urban forest information. Urban Forestry & Urban Greening. 6: 15-22. 6.
Wohlfahrt, G., Tomelleri, E., Hammerle, A., 2019. The urban imprint on plant phenology. Nat Ecol Evol 3, 1668–1674. https://doi.org/10.1038/s41559-019-1017-9
Wood, S.N., 2017. Generalized additive models: An introduction with R. CRC press.
Yamori, W., Hikosaka, K., Way, D.A., 2014. Temperature response of photosynthesis in C3, C4, and CAM plants: Temperature acclimation and temperature adaptation. Photosynth Res 119, 101–117. https://doi.org/10.1007/s11120-013-9874-6
Zhao, S., Liu, S., Zhou, D., 2016. Prevalent vegetation growth enhancement in urban environment. PNAS 113, 6313–6318. https://doi.org/10.1073/pnas.1602312113

6.0.1 Colophon

This report was generated on 2021-10-27 10:52:10 using the following computational environment and dependencies:

#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 4.1.0 (2021-05-18)
#>  os       Ubuntu 20.04.3 LTS          
#>  system   x86_64, linux-gnu           
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  ctype    en_US.UTF-8                 
#>  tz       Europe/Berlin               
#>  date     2021-10-27                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  ! package       * version  date       lib source        
#>  P abind         * 1.4-5    2016-07-21 [?] CRAN (R 4.1.0)
#>  P ape             5.5      2021-04-25 [?] CRAN (R 4.1.0)
#>  P assertthat      0.2.1    2019-03-21 [?] CRAN (R 4.0.2)
#>  P backports       1.1.10   2020-09-15 [?] CRAN (R 4.1.0)
#>  P base64url       1.4      2018-05-14 [?] CRAN (R 4.1.0)
#>  P blob            1.2.1    2020-01-20 [?] CRAN (R 4.1.0)
#>  P bookdown      * 0.21     2020-10-13 [?] CRAN (R 4.1.0)
#>  P boot            1.3-28   2021-05-03 [?] CRAN (R 4.0.5)
#>  P broom           0.7.9    2021-07-27 [?] CRAN (R 4.1.0)
#>  P callr           3.7.0    2021-04-20 [?] CRAN (R 4.1.0)
#>  P cellranger      1.1.0    2016-07-27 [?] CRAN (R 4.1.0)
#>  P ckanr         * 0.5.0    2020-07-30 [?] CRAN (R 4.1.0)
#>  P class           7.3-19   2021-05-03 [?] CRAN (R 4.0.5)
#>  P classInt        0.4-3    2020-04-07 [?] CRAN (R 4.1.0)
#>  P cli             3.0.0    2021-06-30 [?] CRAN (R 4.1.0)
#>  P codetools       0.2-18   2020-11-04 [?] CRAN (R 4.0.3)
#>  P colorspace      1.4-1    2019-03-18 [?] CRAN (R 4.1.0)
#>  P crayon          1.4.1    2021-02-08 [?] CRAN (R 4.1.0)
#>  P crul            1.0.0    2020-07-30 [?] CRAN (R 4.1.0)
#>  P curl            4.3      2019-12-02 [?] CRAN (R 4.0.2)
#>  P data.table    * 1.13.2   2020-10-19 [?] CRAN (R 4.1.0)
#>  P DBI           * 1.1.0    2019-12-15 [?] CRAN (R 4.1.0)
#>  P dbplyr          1.4.4    2020-05-27 [?] CRAN (R 4.1.0)
#>  P desc            1.2.0    2018-05-01 [?] CRAN (R 4.1.0)
#>  P devtools        2.3.1    2020-07-21 [?] CRAN (R 4.1.0)
#>  P dichromat       2.0-0    2013-01-24 [?] CRAN (R 4.1.0)
#>  P digest          0.6.27   2020-10-24 [?] CRAN (R 4.1.0)
#>  P dplR            1.7.2    2021-01-31 [?] CRAN (R 4.1.0)
#>  P dplyr         * 1.0.7    2021-06-18 [?] CRAN (R 4.1.0)
#>  P drake         * 7.13.2   2021-04-22 [?] CRAN (R 4.1.0)
#>  P e1071           1.7-4    2020-10-14 [?] CRAN (R 4.1.0)
#>  P ecmwfr        * 1.3.0    2020-07-13 [?] CRAN (R 4.1.0)
#>  P ellipsis        0.3.2    2021-04-29 [?] CRAN (R 4.1.0)
#>  P evaluate        0.14     2019-05-28 [?] CRAN (R 4.0.2)
#>  P extrafont     * 0.17     2014-12-08 [?] CRAN (R 4.1.0)
#>  P extrafontdb     1.0      2012-06-11 [?] CRAN (R 4.1.0)
#>  P fansi           0.4.1    2020-01-08 [?] CRAN (R 4.0.2)
#>  P farver          2.0.3    2020-01-16 [?] CRAN (R 4.1.0)
#>  P fasterize     * 1.0.3    2020-07-27 [?] CRAN (R 4.1.0)
#>  P filelock        1.0.2    2018-10-05 [?] CRAN (R 4.1.0)
#>  P FNN             1.1.3    2019-02-15 [?] CRAN (R 4.1.0)
#>  P forcats       * 0.5.0    2020-03-01 [?] CRAN (R 4.0.2)
#>  P fs            * 1.5.0    2020-07-31 [?] CRAN (R 4.1.0)
#>  P furrr         * 0.2.0    2020-10-12 [?] CRAN (R 4.1.0)
#>  P future        * 1.19.1   2020-09-22 [?] CRAN (R 4.1.0)
#>  P future.callr  * 0.5.0    2019-09-28 [?] CRAN (R 4.1.0)
#>  P generics        0.0.2    2018-11-29 [?] CRAN (R 4.1.0)
#>  P ggplot2       * 3.3.5    2021-06-25 [?] CRAN (R 4.1.0)
#>  P globals         0.13.1   2020-10-11 [?] CRAN (R 4.1.0)
#>  P glue            1.4.2    2020-08-27 [?] CRAN (R 4.1.0)
#>  P gstat           2.0-7    2021-03-19 [?] CRAN (R 4.1.0)
#>  P gtable          0.3.0    2019-03-25 [?] CRAN (R 4.1.0)
#>  P here          * 0.1      2017-05-28 [?] CRAN (R 4.1.0)
#>  P highr           0.8      2019-03-20 [?] CRAN (R 4.0.2)
#>  P hms             1.1.0    2021-05-17 [?] CRAN (R 4.1.0)
#>  P htmltools       0.5.1.1  2021-01-22 [?] CRAN (R 4.1.0)
#>  P httpcode        0.3.0    2020-04-10 [?] CRAN (R 4.1.0)
#>  P httr          * 1.4.2    2020-07-20 [?] CRAN (R 4.1.0)
#>  P igraph          1.2.6    2020-10-06 [?] CRAN (R 4.1.0)
#>  P intervals       0.15.2   2020-04-04 [?] CRAN (R 4.1.0)
#>  P jsonlite        1.7.2    2020-12-09 [?] CRAN (R 4.1.0)
#>  P kableExtra    * 1.3.4    2021-02-20 [?] CRAN (R 4.1.0)
#>  P KernSmooth      2.23-20  2021-05-03 [?] CRAN (R 4.0.5)
#>  P knitr           1.33     2021-04-24 [?] CRAN (R 4.1.0)
#>  P labeling        0.4.2    2020-10-20 [?] CRAN (R 4.1.0)
#>  P lattice         0.20-44  2021-05-02 [?] CRAN (R 4.1.0)
#>  P lifecycle       1.0.0    2021-02-15 [?] CRAN (R 4.1.0)
#>  P listenv         0.8.0    2019-12-05 [?] CRAN (R 4.1.0)
#>  P lme4          * 1.1-27   2021-05-15 [?] CRAN (R 4.1.0)
#>  P lwgeom          0.2-5    2020-06-12 [?] CRAN (R 4.1.0)
#>  P magrittr      * 2.0.1    2020-11-17 [?] CRAN (R 4.1.0)
#>  P mapproj         1.2.7    2020-02-03 [?] CRAN (R 4.1.0)
#>  P maps            3.3.0    2018-04-03 [?] CRAN (R 4.1.0)
#>  P MASS            7.3-54   2021-05-03 [?] CRAN (R 4.0.5)
#>  P Matrix        * 1.3-3    2021-05-04 [?] CRAN (R 4.1.0)
#>  P matrixStats     0.57.0   2020-09-25 [?] CRAN (R 4.1.0)
#>  P memoise         1.1.0    2017-04-21 [?] CRAN (R 4.1.0)
#>  P mgcv            1.8-35   2021-04-18 [?] CRAN (R 4.1.0)
#>  P minqa           1.2.4    2014-10-09 [?] CRAN (R 4.1.0)
#>  P munsell         0.5.0    2018-06-12 [?] CRAN (R 4.1.0)
#>  P ncdf4         * 1.17     2019-10-23 [?] CRAN (R 4.1.0)
#>  P nlme            3.1-152  2021-02-04 [?] CRAN (R 4.0.3)
#>  P nloptr          1.2.2.2  2020-07-02 [?] CRAN (R 4.1.0)
#>  P pals            1.7      2021-04-17 [?] CRAN (R 4.1.0)
#>  P patchwork     * 1.1.1    2020-12-17 [?] CRAN (R 4.1.0)
#>  P pillar          1.6.1    2021-05-16 [?] CRAN (R 4.1.0)
#>  P pkgbuild        1.1.0    2020-07-13 [?] CRAN (R 4.1.0)
#>  P pkgconfig       2.0.3    2019-09-22 [?] CRAN (R 4.0.2)
#>  P pkgload         1.1.0    2020-05-29 [?] CRAN (R 4.1.0)
#>  P plyr            1.8.6    2020-03-03 [?] CRAN (R 4.1.0)
#>  P png             0.1-7    2013-12-03 [?] CRAN (R 4.1.0)
#>  P prettyunits     1.1.1    2020-01-24 [?] CRAN (R 4.1.0)
#>  P processx        3.5.2    2021-04-30 [?] CRAN (R 4.1.0)
#>  P progress        1.2.2    2019-05-16 [?] CRAN (R 4.1.0)
#>  P ps              1.4.0    2020-10-07 [?] CRAN (R 4.1.0)
#>  P purrr         * 0.3.4    2020-04-17 [?] CRAN (R 4.0.2)
#>  P R.methodsS3     1.8.1    2020-08-26 [?] CRAN (R 4.1.0)
#>  P R.oo            1.24.0   2020-08-26 [?] CRAN (R 4.1.0)
#>  P R.utils         2.11.0   2021-09-26 [?] CRAN (R 4.1.0)
#>  P R6              2.5.0    2020-10-28 [?] CRAN (R 4.1.0)
#>  P ragg            1.1.3    2021-06-09 [?] CRAN (R 4.1.0)
#>  P raster        * 3.4-10   2021-05-03 [?] CRAN (R 4.1.0)
#>  P RColorBrewer    1.1-2    2014-12-07 [?] CRAN (R 4.1.0)
#>  P Rcpp            1.0.6    2021-01-15 [?] CRAN (R 4.1.0)
#>  P readxl        * 1.3.1    2019-03-13 [?] CRAN (R 4.1.0)
#>  P remotes         2.2.0    2020-07-21 [?] CRAN (R 4.1.0)
#>    renv            0.13.2   2021-03-30 [1] CRAN (R 4.1.0)
#>  P rgdal         * 1.5-18   2020-10-13 [?] CRAN (R 4.1.0)
#>  P rlang         * 0.4.11   2021-04-30 [?] CRAN (R 4.1.0)
#>  P rmarkdown     * 2.5      2020-10-21 [?] CRAN (R 4.1.0)
#>  P rnaturalearth * 0.1.0    2017-03-21 [?] CRAN (R 4.1.0)
#>  P rprojroot       1.3-2    2018-01-03 [?] CRAN (R 4.1.0)
#>  P rstudioapi      0.11     2020-02-07 [?] CRAN (R 4.1.0)
#>  P Rttf2pt1        1.3.8    2020-01-10 [?] CRAN (R 4.1.0)
#>  P rvest           1.0.0    2021-03-09 [?] CRAN (R 4.1.0)
#>  P scales        * 1.1.1    2020-05-11 [?] CRAN (R 4.1.0)
#>  P sessioninfo     1.1.1    2018-11-05 [?] CRAN (R 4.1.0)
#>  P sf            * 0.9-6    2020-09-13 [?] CRAN (R 4.1.0)
#>  P signal          0.7-7    2021-05-25 [?] CRAN (R 4.1.0)
#>  P snow          * 0.4-3    2018-09-14 [?] CRAN (R 4.1.0)
#>  P sp            * 1.4-5    2021-01-10 [?] CRAN (R 4.1.0)
#>  P spacetime       1.2-5    2021-06-14 [?] CRAN (R 4.1.0)
#>  P stars         * 0.4-3    2020-07-08 [?] CRAN (R 4.1.0)
#>  P storr           1.2.4    2020-10-12 [?] CRAN (R 4.1.0)
#>  P stringi         1.5.3    2020-09-09 [?] CRAN (R 4.1.0)
#>  P stringr       * 1.4.0    2019-02-10 [?] CRAN (R 4.0.2)
#>  P svglite         2.0.0    2021-02-20 [?] CRAN (R 4.1.0)
#>  P systemfonts     1.0.2    2021-05-11 [?] CRAN (R 4.1.0)
#>  P testthat        3.0.4    2021-07-01 [?] CRAN (R 4.1.0)
#>  P textshaping     0.3.5    2021-06-09 [?] CRAN (R 4.1.0)
#>  P tibble        * 3.0.4    2020-10-12 [?] CRAN (R 4.1.0)
#>  P tidyr           1.1.2    2020-08-27 [?] CRAN (R 4.1.0)
#>  P tidyselect      1.1.0    2020-05-11 [?] CRAN (R 4.0.2)
#>  P txtq            0.2.3    2020-06-23 [?] CRAN (R 4.1.0)
#>  P units           0.6-7    2020-06-13 [?] CRAN (R 4.1.0)
#>  P usethis         1.6.1    2020-04-29 [?] CRAN (R 4.1.0)
#>  P utf8            1.1.4    2018-05-24 [?] CRAN (R 4.0.2)
#>  P vctrs           0.3.8    2021-04-29 [?] CRAN (R 4.1.0)
#>  P viridisLite     0.4.0    2021-04-13 [?] CRAN (R 4.1.0)
#>  P webshot         0.5.2    2019-11-22 [?] CRAN (R 4.1.0)
#>  P withr           2.4.2    2021-04-18 [?] CRAN (R 4.1.0)
#>  P xfun            0.24     2021-06-15 [?] CRAN (R 4.1.0)
#>  P XML             3.99-0.8 2021-09-17 [?] CRAN (R 4.1.0)
#>  P xml2          * 1.3.2    2020-04-23 [?] CRAN (R 4.1.0)
#>  P xts             0.12.1   2020-09-09 [?] CRAN (R 4.1.0)
#>  P yaml            2.2.1    2020-02-01 [?] CRAN (R 4.0.2)
#>  P zoo             1.8-9    2021-03-09 [?] CRAN (R 4.1.0)
#> 
#> [1] /home/hurley/_work/renv/berlin.trees-c2f6692a/R-4.1/x86_64-pc-linux-gnu
#> [2] /tmp/RtmpZtJ0Mn/renv-system-library
#> [3] /tmp/Rtmpdomfdo/renv-system-library
#> 
#>  P ── Loaded and on-disk path mismatch.

The current Git commit details are:

#> Local:    master /home/hurley/_work/p_024_GFZ_berlin_trees/berlin.trees
#> Remote:   master @ origin (https://github.com/the-Hull/berlin.trees)
#> Head:     [73b3047] 2021-10-26: Merge branch 'master' of https://github.com/the-Hull/berlin.trees